---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-unipelt
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sentiment-unipelt

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2928
- Accuracy: 0.9023
- Precision: 0.8842
- Recall: 0.8783
- F1: 0.8812

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5535        | 1.0   | 122  | 0.4992          | 0.7293   | 0.6646    | 0.6285 | 0.6373 |
| 0.444         | 2.0   | 244  | 0.4053          | 0.8170   | 0.7847    | 0.8256 | 0.7961 |
| 0.3464        | 3.0   | 366  | 0.3425          | 0.8421   | 0.8345    | 0.7683 | 0.7905 |
| 0.2852        | 4.0   | 488  | 0.3136          | 0.8722   | 0.8445    | 0.8496 | 0.8470 |
| 0.2608        | 5.0   | 610  | 0.3060          | 0.8722   | 0.8445    | 0.8496 | 0.8470 |
| 0.2415        | 6.0   | 732  | 0.3100          | 0.8647   | 0.8325    | 0.8642 | 0.8447 |
| 0.2329        | 7.0   | 854  | 0.2860          | 0.8847   | 0.8567    | 0.8734 | 0.8642 |
| 0.199         | 8.0   | 976  | 0.2879          | 0.8872   | 0.8672    | 0.8577 | 0.8622 |
| 0.1939        | 9.0   | 1098 | 0.2826          | 0.8897   | 0.8659    | 0.8695 | 0.8676 |
| 0.1806        | 10.0  | 1220 | 0.2982          | 0.8797   | 0.8795    | 0.8224 | 0.8439 |
| 0.1674        | 11.0  | 1342 | 0.2735          | 0.8947   | 0.8730    | 0.8730 | 0.8730 |
| 0.1553        | 12.0  | 1464 | 0.2753          | 0.8947   | 0.8757    | 0.8680 | 0.8717 |
| 0.1431        | 13.0  | 1586 | 0.2937          | 0.8922   | 0.8785    | 0.8562 | 0.8662 |
| 0.1417        | 14.0  | 1708 | 0.2911          | 0.9073   | 0.8823    | 0.9019 | 0.8910 |
| 0.1236        | 15.0  | 1830 | 0.2956          | 0.9023   | 0.8828    | 0.8808 | 0.8818 |
| 0.1304        | 16.0  | 1952 | 0.3011          | 0.9023   | 0.8773    | 0.8933 | 0.8846 |
| 0.1164        | 17.0  | 2074 | 0.2943          | 0.8997   | 0.8778    | 0.8816 | 0.8797 |
| 0.1144        | 18.0  | 2196 | 0.2937          | 0.8972   | 0.8732    | 0.8823 | 0.8776 |
| 0.1198        | 19.0  | 2318 | 0.2985          | 0.8972   | 0.8812    | 0.8673 | 0.8738 |
| 0.1104        | 20.0  | 2440 | 0.2928          | 0.9023   | 0.8842    | 0.8783 | 0.8812 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2